This invention relates to use of a computer based technique to predict brain disease, brain degenerative disease and atrophy as well as other psychiatric illnesses before their onset. The brains of people with Alzheimer show early atrophy before onset of diseased symptoms. Schizophrenia patients show minor changes even before their first psychotic episode. That raises the possibility of screening and early diagnosis for the disease and early intervention for people at risk.
This invention is an automated tool comprising a computed algorithm for the sake of providing automated early diagnosis of disease and psychiatric conditions.
There are many tools and procedures for obtaining brain scan images. Likewise, there are countless algorithms and methods intended to improve scan images using image processing and feature extraction and classifier/discriminant function techniques. Among these are those described in the following publications (not all of which are valid prior art) whose contents are incorporated herein by reference:
“Fuzzy region integration approach for subcortical structure segmentation” by Shichun Peng et al. Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'OZ), Vol. 3;
“Automated Characterization and Recognition of 2D and 3D Brain Structure in MRI for Diagnostic Support” by Rasmus Larsen, Lars Hanson & Karl Skoglund et al. Apr. 10, 2003 shown in a PhD Project Poster presentation at the Technical University of Denmark (Informatics and Mathematical Modeling)
“Pattern of cerebral hypoperfusion in Alzheimer disease and mild cognitive impairment measured with arterial spin-labeling MR imaging” by Johnson NA et al. Radiology 2005 March; 234 (3): 851-9;
“Discrimination between Alzheimer dementia and controls by automated analysis of multicentre FDG PET” Neuroimage 2002 September: 17 (1) 302-16.
There numerous U.S. patents on segmentation and image processing of human tissue including for example U.S. Pat. Nos.: 4,991,092; 5,812,691; 6,718,055; 4,922,915; 4,736,751; 6,952,097; 6,574,566; 4,991,092; and 5,873,823.
Two relevant publications published well after the priority date of the present application are U.S. Patent Application No. 2003/0228042 entitled “Method and system for preparation of customized imaging atlas and registration with patient image”, and a corresponding research paper entitled: “Image Study Summarization of MR Brain Images by Automated Localization of Relevant Structures”, by USHA SINHA et al., Ann. N.Y. Acad. Sci. 980: 278-286 (2002). The research paper deals with object segmentation and localization having a final goal as defined by its authors “. . . to identify relevant slices of an imaging study that has several important applications in image integration with the electronic medical record, in automated creation of teaching files, and in clinical compression.” The paper discusses a methodology to objectify the patient presenting condition by automated selection of relevant images from a serial MR study. Structured data entry is used to capture the patient's chief complaint, pertinent history, signs, and symptoms. Expert created rules use this data to arrive at a differential and to identify the affected brain region/structure. Another expert created knowledge base then maps this information to the relevant image type, including image sequence specifics and orientation. A DICOM study reader identifies the relevant imaging sequences from the MR study. The structure localization method involves a search based on principal component analysis. A training set of subimages containing the structure of interest is used to generate a basis set of prototype images called eigenimages. The structure is located in an image by searching the image for a subregion that best matches the basis set. The structure localization was used to locate the lateral ventricles and orbits in nine images that were not part of the training set. The automated localizations were compared to expert localizations and the center of the regions located by the two techniques agreed to within ±1.7 mm. (average for the nine localizations each of two structures).
The contents of all the above-mentioned research papers, patents and patent applications are incorporated herein by reference.
Efficient automated diagnostic tools for brain scan images have one thing in common. They must all contain within their algorithms a method of data classification and storage as well as a method for training the classifier using an expert interpreter.
Early treatment with behavioral therapy or drugs could prevent, or at least mitigate, the full onset of Alzheimer or even schizophrenia. The longer the disease or psychosis goes untreated, the worse the outcome. Alzheimer and Schizophrenia is probably the most expensive diseases for the National Health Service of any country. If it can be prevented by early detection, the implications are vast.
Magnetic resonance imaging (MRI) in brain scans showed significant differences between healthy brains versus those of patients. The brain changes began some time before the Alzheimer or schizophrenic patients first suffered dementia or a psychotic episode.
Over the clinical course of Alzheimer, patients demonstrate progressive declines in functional ability that correlate with MMSE scores. In the preclinical phase, also called MCI, patients with MMSE score greater than 23 will demonstrate minimal impairment—generally, mild memory loss—while functioning normally and independently.
Atrophy rates for brain temporal lobe, cortex, Amygdalae, temporal gyrus, hippocampus, and entorhinal cortices are significantly increased in patients compared with controls. Linear extrapolation backward suggested medial temporal lobe atrophy commenced 3.5 years before onset of symptoms, when all patients were asymptomatic. Medial temporal lobe atrophy rates are an early and distinguishing feature of Alzheimer. Atrophy rates for brain, temporal lobe, hippocampus, and entorhinal cortices are significantly increased in patients compared with controls.
Schizophrenia patients have significant deficits in cortical gray matter and in temporal lobe gray matter. The temporal lobes of the brain are linked with speech and the experience of hallucinations. There were also significant differences in whole brain volume, as well as significant enlargement of the lateral and third ventricles. Structural deviations were found in both untreated and minimally treated subjects. No relationships were found between any brain matter volumes and positive or negative symptoms. Structural brain abnormalities were distributed throughout the cortex with particular decrement evident in gray matter. This feature is consistent with altered cell structure and disturbed neuronal connectivity, which accounts for the functional abnormality of psychosis. These brain abnormalities were not specific to schizophrenia; they were also present in the brains of people suffering from other kinds of psychosis, such as bipolar disorder. It is assumed that many mental illnesses begin with the same changes in brain structure and chemistry and that an initial common pathway diverges into different forms of mental illness. This means that treating anyone showing signs of the brain abnormalities should prevent the onset of other mental diseases as well.
The process of decoding and analyzing brain scan images so as to provide an accurate psychiatric profile of individuals is difficult if not virtually impossible to provide under human evaluation.
U.S. Pat. No. 5,632,276 (Eidelberg et al.) discloses a method and apparatus for screening patients for nervous system dysfunction including neurological capacity and dysfunction. A patient profile of actual functional activity of a brain of a patient is produced and compared with at least one marker. The marker is a profile of predetermined functional activity at a plurality of sets of predetermined coordinates of a given brain geometry. It appears that the database search and comparison are performed in respect of different data associated with a single patient.
U.S. Pat. Nos. 6,205,236, 5,999,639 and 6,115,488 to Rogers et al. disclose a method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm. The results of the system are optimally combined with a radiologist's observation of the original mammogram by combining the observations with the results, after the radiologist has first accepted or rejected individual detections reported by the system.
Reference is also made to “Alzheimer's disease and models of computation: Imaging, classification, and neural models” by Hojjat Adeli et al. appearing in Journal of Alzheimer's Disease 7 (2005) 187-199. Although this article is not prior art to the present application, which derives from PCT/IL01/01047 filed Nov. 12, 2001 and claiming Priority from IL 139655 filed Nov. 14, 2000, it is nevertheless interesting for its conclusion which appears to corroborate the earlier researches of the present inventor. Thus, particular reference is made to the conclusion which notes that researchers have not yet found conclusive evidence regarding the specificity and sensitivity of the neurological markers and diagnostic techniques based on them for the diagnosis of Alzheimer's disease. Similarly, there seems to be no consensus regarding the various hypotheses of progression of AD from the point of view of different disease states (such as MCI, PDAT, and SDAT) and clear cut boundaries between them. It was concluded that a combination of parameters from different investigation modalities seems to be the way to go for increasing the accuracy of detection and diagnosis.
In a research paper entitled “MRI and CSF studies in the early diagnosis of Alzheimer's Disease” by M. J. de Leon et al. appearing in Journal of Internal Medicine 2004; 256: 205-223 it is noted that the combined use of MRI and cerebrospinal fluid diagnostic measures for Alzheimer's Disease has the promise to improve the early and specific diagnosis of Alzheimer's Disease.
There is no suggestion in the scientific or patent literature preceding the priority date of the present application to facilitate the diagnosis of brain disease in general, and Alzheimer's Disease in particular, by searching a database containing parameters associated with at least one feature of a plurality of brain scan images each compiled from respective patient data and inserted into the database so as to extract from the database a set of respective parameters each associated with a feature of interest and wherein in respect of each feature at least one of the corresponding parameters is indicative of a brain disease profile; and analyzing the respective parameters to determine a statistically significant brain disease profile which fits a patient based on the at least one feature of interest of a brain scan image of the patient.
Therefore, it would be desirable to provide a method and system to diagnose and profile brain disease such as dementia (especially Alzheimer) and psychiatric illness using a database of brain scan images and associated parameters.